Driver variability is a large factor in vehicle performance, such as in regard to fuel economy and emissions. For example, Driver A may consistently gradually accelerate a vehicle while Driver B consistently demands high acceleration (e.g., depresses the accelerator pedal more than fifty percent each time acceleration is desired). As a result, Driver B may achieve relatively worse fuel economy than Driver A. This undesirable trait will be even more true in the future as other automated optimizations take performance variability out of the equation, leaving the variability of driver performance as the largest remaining piece.
However, driver performance management is notoriously difficult in the controls universe, as drivers have infinite capacity to vary behavior in response to control variations. Further, previous driver management schemes are punitive—limiting available torque or speed in response to suboptimal driver performance—or extremely simple (e.g., providing greater speed or power in cruise control). These schemes can be effective, but only in a limited manner as they cannot help poor drivers imitate the behaviors of good drivers, but merely reduce the consequences of poor driving performance. Additionally, previously known systems cannot adapt to multiple drivers, meaning if a good driver follows a poor driver, or a poor driver follows a good driver, the system will not respond in the desired manner, and additionally the system may have a fixed response to a poor driver even though the driver may be poor for different reasons or at different operating conditions. In this regard, such systems tend to be binary, where the reward or penalty is present or not, and accordingly, they cannot adjust response time in an intelligent manner. Still further, presently known systems cannot affect the dynamic behavior of the vehicle to the driver, because they only affect limits or maximum values (speed or torque limits, benefits in max gear cruising, etc.) and do not affect transient behavior where many of the losses in efficiency from poor driving are likely to occur. Thus, a need exists for improved driver optimization systems, methods, and apparatuses.